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library(ggplot2)
library(readr)
library(tidyr)
library(dplyr)

Attaching package: ‘dplyr’

The following objects are masked from ‘package:stats’:

    filter, lag

The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union

# Read the CSV file
data <- read_csv("../../ProcessedData/final_vegePrice0.csv")

Rows: 132 Columns: 2756── Column specification ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr    (3): Commodity, Unit, Category
dbl (2753): 2013-06-16, 2013-06-17, 2013-06-18, 2013-06-19, 2013-06-20, 2013-06-21, 2013-06-25, 2013-06-26, 2013-06-27, 2013-06-28, 2013-06-30, 2013-07-01, 2013-07-02, 2013-07-03, 2013-07-04, ...
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

# Convert the data from wide to long format
long_data <- data %>%
  pivot_longer(cols = starts_with("20"), names_to = "Date", values_to = "Price") %>%
  mutate(Date = as.Date(Date))

# Plotting all commodities
ggplot(long_data, aes(x = Date, y = Price, group = Commodity, color = Commodity)) +
  geom_line() +
  theme_minimal() +
  labs(title = "Price Trends of All Commodities",
       x = "Date",
       y = "Price") +
  theme(legend.position="none")

# Creating multiple plots, one for each category
plots <- long_data %>%
  group_by(Category) %>%
  do(plot = ggplot(., aes(x = Date, y = Price, group = Commodity, color = Commodity)) +
              geom_line() +
              theme_minimal() +
              labs(title = paste("Price Trends in Category:", unique(.$Category)),
                   x = "Date",
                   y = "Price") +
              theme(legend.position="none"))

# Saving the plots to a list
plot_list <- lapply(plots$plot, print)

# Counting missing values per commodity
missing_values <- long_data %>%
  group_by(Commodity) %>%
  summarize(Missing = sum(is.na(Price)))

# Calculate the total number of dates
total_dates <- nrow(long_data) / n_distinct(long_data$Commodity)

# Adding percentage of missing data
missing_values <- missing_values %>%
  mutate(PercentMissing = (Missing / total_dates) * 100)

# Print the number of missing values and their percentage
print(missing_values)

NA

print(long_data)

# Filter out commodities with more than 10% missing data
commodities_to_keep <- missing_values %>%
  filter(PercentMissing <= 10) %>%
  select(Commodity)

# Filter the long_data dataframe to only include the selected commodities
filtered_long_data <- long_data %>%
  filter(Commodity %in% commodities_to_keep$Commodity)

print(filtered_long_data)

NA

plots <- filtered_long_data %>%
  group_by(Category) %>%
  do(plot = ggplot(., aes(x = Date, y = Price, group = Commodity, color = Commodity)) +
              geom_line() +
              theme_minimal() +
              labs(title = paste("Price Trends in Category:", unique(.$Category)),
                   x = "Date",
                   y = "Price") +
              theme(legend.position="none"))

# Saving the plots to a list
plot_list <- lapply(plots$plot, print)

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